We can get some insights from the figures.
First, HONAM can learn soft (or smooth) nonlinear functions because it uses neural networks.
Because the conventional GAM can only learn linear functions, its expressive power is limited.
In addition, because tree-based models, such as boosting trees, can only learn discretized functions, they require a massive number of trees to learn smooth-looking functions.
Therefore, it is difficult to extend various settings, such as multi-label or multi-class learning [1]}, and their explanations are difficult to understand.
Second, HONAM can learn sharp jumps.
In the California Housing dataset, HONAM learned a few sharp jumps.
We believe that our ExpDive unit is more effective in a field where sharp jumps frequently occur, such as in stock price predictions.
Third, we can view the relationships between features via HONAM.
In other words, HONAM provides more detailed interpretations than NAM and other interpretable methods.
